Developing AI Agents: From Simple to Complex

AI agents represent the next evolution of artificial intelligence, going far beyond simple chatbots. In this article, we'll explore the process of developing AI agents from basic concepts to complex autonomous systems.

What is an AI Agent?
An AI agent is a software system that:

• Makes autonomous decisions
• Interacts with the environment
• Learns from experience
• Has a specific goal or set of goals
• Can collaborate with other agents

AI Agent Development Stages:

1. Architecture Planning

• Defining goals and constraints
• Choosing decision-making model
• Designing learning system
• Planning environment interaction

2. Basic Implementation

  constructor(
    private model: LLM,
    private memory: Memory,
    private tools: Tool[]
  ) {}

  async think(input: string): Promise<string> {
    const context = await this.memory.getRelevantContext(input);
    const plan = await this.model.plan(input, context);
    return await this.executeActions(plan);
  }

  private async executeActions(plan: Action[]): Promise<string> {
    // Action execution logic
  }
}

3. Decision Making Systems

• Large Language Models (LLM)
• Rule-based systems
• Neural networks
• Hybrid approaches

4. Memory Management

• Short-term memory
• Long-term memory
• Contextual memory
• Vector databases

5. Tools and Capabilities

• API integrations
• Data access
• Action execution
• User interaction

6. Training and Optimization

• Reinforcement learning
• Imitation learning
• Active learning
• Meta-learning

7. Security and Ethics

• Access restrictions
• Action validation
• Ethical principles
• Behavior monitoring

Simple AI Agent Implementation Example:

  store(key: string, value: any): Promise<void>;
  retrieve(key: string): Promise<any>;
  getRelevantContext(input: string): Promise<string>;
}

interface Tool {
  name: string;
  description: string;
  execute(input: string): Promise<string>;
}

class SimpleAIAgent {
  private memory: Memory;
  private tools: Tool[];
  private model: LLM;

  constructor() {
    this.memory = new VectorMemory();
    this.tools = this.initializeTools();
    this.model = new LLMModel();
  }

  async process(input: string): Promise<string> {
    // 1. Get context
    const context = await this.memory.getRelevantContext(input);
    
    // 2. Analyze input
    const analysis = await this.model.analyze(input, context);
    
    // 3. Plan actions
    const plan = await this.model.createPlan(analysis);
    
    // 4. Execute actions
    const result = await this.executePlan(plan);
    
    // 5. Store result
    await this.memory.store(input, result);
    
    return result;
  }

  private async executePlan(plan: Action[]): Promise<string> {
    let result = '';
    for (const action of plan) {
      const tool = this.tools.find(t => t.name === action.tool);
      if (tool) {
        result += await tool.execute(action.input);
      }
    }
    return result;
  }
}

Advanced Capabilities:

1. Multi-agent Systems

• Agent coordination
• Task distribution
• Collaborative learning
• Conflict resolution

2. Adaptive Behavior

• Dynamic strategy adjustment
• Feedback analysis
• Performance optimization

3. Specialization

• Data analysis agents
• Decision-making agents
• User interaction agents
• Task execution agents

Conclusions:
Developing AI agents is a complex but exciting process that opens new possibilities in business process automation and optimization. The key to success lies in proper architecture planning, choosing the right tools, and continuous system improvement based on experience.